Inferring high-frequent mixed urban functions from telecom traffic

Precise distinction of mixed functions on urban land is essential for urban studies and planning, while existing methods are limited by high sampling bias, low observation frequency, and lack of semantic information in common data sources. In this paper, we introduce a new proxy for human behavior, the telecom traffic data as a remedy to the above limitations, and present an analytical framework which utilizes anonymized and aggregated telecom traffic data to infer mixed urban functions at spatiotemporal granularities as fine as buildings and hours. A time-series decomposition method is designed to map the mixture of urban functions, which is further refined by a hierarchical agglomerative clustering method taking urban textures as an additional source of information. In a case study in Shenzhen, China, we find the function of urban buildings can be decomposed into the mixture of three basic functions, namely dwelling, work, and recreation. We further find that the introduction of urban texture information helps identify particular forms of functional combination, which indicate special-function buildings such as urban villages and roadside shops. This study implies ways to improve urban management through methodological contributions in mixed urban function identification alongside the introduction of the telecom traffic, a kind of high-frequency urban data, and also helps inspire a rethinking of the form/function dichotomy in the era of High-frequent cities.

Automated summary

Accurate distinction of mixed functions on urban land is crucial for urban studies and planning. This paper introduces a new method that utilizes telecom traffic data to infer mixed urban functions at fine spatiotemporal granularities. The study finds that urban buildings can be decomposed into a mixture of dwelling, work, and recreation functions, with the introduction of urban texture information helping to identify special-function buildings. This research contributes to improving urban management through methodological advancements in mixed urban function identification, and highlights the importance of high-frequency urban data like telecom traffic in the era of High-frequent cities.